{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"creditcard.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>...</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "      <td>284807.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>88.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.15</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "      <td>250.12</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-56.41</td>\n",
       "      <td>-72.72</td>\n",
       "      <td>-48.33</td>\n",
       "      <td>-5.68</td>\n",
       "      <td>-113.74</td>\n",
       "      <td>-26.16</td>\n",
       "      <td>-43.56</td>\n",
       "      <td>-73.22</td>\n",
       "      <td>-13.43</td>\n",
       "      <td>...</td>\n",
       "      <td>-34.83</td>\n",
       "      <td>-10.93</td>\n",
       "      <td>-44.81</td>\n",
       "      <td>-2.84</td>\n",
       "      <td>-10.30</td>\n",
       "      <td>-2.60</td>\n",
       "      <td>-22.57</td>\n",
       "      <td>-15.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.50</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>5.60</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.50</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.60</td>\n",
       "      <td>...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>77.16</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>22.06</td>\n",
       "      <td>9.38</td>\n",
       "      <td>16.88</td>\n",
       "      <td>34.80</td>\n",
       "      <td>73.30</td>\n",
       "      <td>120.59</td>\n",
       "      <td>20.01</td>\n",
       "      <td>15.59</td>\n",
       "      <td>...</td>\n",
       "      <td>27.20</td>\n",
       "      <td>10.50</td>\n",
       "      <td>22.53</td>\n",
       "      <td>4.58</td>\n",
       "      <td>7.52</td>\n",
       "      <td>3.52</td>\n",
       "      <td>31.61</td>\n",
       "      <td>33.85</td>\n",
       "      <td>25691.16</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time        V1        V2        V3        V4        V5        V6  \\\n",
       "count 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00   \n",
       "mean   94813.86      0.00      0.00     -0.00      0.00      0.00      0.00   \n",
       "std    47488.15      1.96      1.65      1.52      1.42      1.38      1.33   \n",
       "min        0.00    -56.41    -72.72    -48.33     -5.68   -113.74    -26.16   \n",
       "25%    54201.50     -0.92     -0.60     -0.89     -0.85     -0.69     -0.77   \n",
       "50%    84692.00      0.02      0.07      0.18     -0.02     -0.05     -0.27   \n",
       "75%   139320.50      1.32      0.80      1.03      0.74      0.61      0.40   \n",
       "max   172792.00      2.45     22.06      9.38     16.88     34.80     73.30   \n",
       "\n",
       "             V7        V8        V9  ...       V21       V22       V23  \\\n",
       "count 284807.00 284807.00 284807.00  ... 284807.00 284807.00 284807.00   \n",
       "mean      -0.00      0.00     -0.00  ...      0.00     -0.00      0.00   \n",
       "std        1.24      1.19      1.10  ...      0.73      0.73      0.62   \n",
       "min      -43.56    -73.22    -13.43  ...    -34.83    -10.93    -44.81   \n",
       "25%       -0.55     -0.21     -0.64  ...     -0.23     -0.54     -0.16   \n",
       "50%        0.04      0.02     -0.05  ...     -0.03      0.01     -0.01   \n",
       "75%        0.57      0.33      0.60  ...      0.19      0.53      0.15   \n",
       "max      120.59     20.01     15.59  ...     27.20     10.50     22.53   \n",
       "\n",
       "            V24       V25       V26       V27       V28    Amount     Class  \n",
       "count 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00 284807.00  \n",
       "mean       0.00      0.00      0.00     -0.00     -0.00     88.35      0.00  \n",
       "std        0.61      0.52      0.48      0.40      0.33    250.12      0.04  \n",
       "min       -2.84    -10.30     -2.60    -22.57    -15.43      0.00      0.00  \n",
       "25%       -0.35     -0.32     -0.33     -0.07     -0.05      5.60      0.00  \n",
       "50%        0.04      0.02     -0.05      0.00      0.01     22.00      0.00  \n",
       "75%        0.44      0.35      0.24      0.09      0.08     77.16      0.00  \n",
       "max        4.58      7.52      3.52     31.61     33.85  25691.16      1.00  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option(\"display.float\", \"{:.2f}\".format)\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "284802    0\n",
       "284803    0\n",
       "284804    0\n",
       "284805    0\n",
       "284806    0\n",
       "Name: Class, Length: 284807, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only non-transformed variables to work with are: Time Amount Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LABELS = [\"Normal\", \"Fraud\"]\n",
    "\n",
    "count_classes = pd.value_counts(data['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.title(\"Transaction Class Distribution\")\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Class.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Fraudulant transactions: (492, 31)\n",
      "Shape of Non-Fraudulant transactions: (284315, 31)\n"
     ]
    }
   ],
   "source": [
    "fraud = data[data['Class']==1]\n",
    "normal = data[data['Class']==0]\n",
    "\n",
    "print(f\"Shape of Fraudulant transactions: {fraud.shape}\")\n",
    "print(f\"Shape of Non-Fraudulant transactions: {normal.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>492.00</td>\n",
       "      <td>284315.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>122.21</td>\n",
       "      <td>88.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>256.68</td>\n",
       "      <td>250.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.25</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>105.89</td>\n",
       "      <td>77.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2125.87</td>\n",
       "      <td>25691.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Amount    Amount\n",
       "count  492.00 284315.00\n",
       "mean   122.21     88.29\n",
       "std    256.68    250.11\n",
       "min      0.00      0.00\n",
       "25%      1.00      5.65\n",
       "50%      9.25     22.00\n",
       "75%    105.89     77.05\n",
       "max   2125.87  25691.16"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([fraud.Amount.describe(), normal.Amount.describe()], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>492.00</td>\n",
       "      <td>284315.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>80746.81</td>\n",
       "      <td>94838.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47835.37</td>\n",
       "      <td>47484.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>406.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41241.50</td>\n",
       "      <td>54230.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75568.50</td>\n",
       "      <td>84711.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>128483.00</td>\n",
       "      <td>139333.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>170348.00</td>\n",
       "      <td>172792.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time      Time\n",
       "count    492.00 284315.00\n",
       "mean   80746.81  94838.20\n",
       "std    47835.37  47484.02\n",
       "min      406.00      0.00\n",
       "25%    41241.50  54230.00\n",
       "50%    75568.50  84711.00\n",
       "75%   128483.00 139333.00\n",
       "max   170348.00 172792.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([fraud.Time.describe(), normal.Time.describe()], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the time feature\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Time Distribution (Seconds)')\n",
    "\n",
    "sns.distplot(data['Time'], color='blue');\n",
    "\n",
    "#plot the amount feature\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('Distribution of Amount')\n",
    "sns.distplot(data['Amount'],color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Time Distribution (Seconds)')\n",
    "\n",
    "sns.distplot(data['Time'], color='blue');\n",
    "\n",
    "#plot the amount feature\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title('Distribution of Amount')\n",
    "sns.distplot(data['Amount'],color='blue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x152062b1d08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data[data.Class == 0].Time.hist(bins=35, color='blue', alpha=0.6)\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "data[data.Class == 1].Time.hist(bins=35, color='blue', alpha=0.6, label=\"Fraudulant Transaction\")\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "data[data.Class == 0].Time.hist(bins=35, color='blue', alpha=0.6, label=\"Non Fraudulant Transaction\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(figsize=(20, 20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap to find any high correlations\n",
    "\n",
    "plt.figure(figsize=(11,9))\n",
    "sns.heatmap(data=data.corr(), cmap=\"seismic\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraudulant transaction weight: 0.0017994745785028623\n",
      "Non-Fraudulant transaction weight: 0.9982005254214972\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scalar = StandardScaler()\n",
    "\n",
    "X = data.drop('Class', axis=1)\n",
    "y = data.Class\n",
    "\n",
    "X_train_v, X_test, y_train_v, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.3, random_state=42)\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_train_v, y_train_v, \n",
    "                                                            test_size=0.2, random_state=42)\n",
    "\n",
    "X_train = scalar.fit_transform(X_train)\n",
    "X_validate = scalar.transform(X_validate)\n",
    "X_test = scalar.transform(X_test)\n",
    "\n",
    "w_p = y_train.value_counts()[0] / len(y_train)\n",
    "w_n = y_train.value_counts()[1] / len(y_train)\n",
    "\n",
    "print(f\"Fraudulant transaction weight: {w_n}\")\n",
    "print(f\"Non-Fraudulant transaction weight: {w_p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING: X_train: (159491, 30), y_train: (159491,)\n",
      "_______________________________________________________\n",
      "VALIDATION: X_validate: (39873, 30), y_validate: (39873,)\n",
      "__________________________________________________\n",
      "TESTING: X_test: (85443, 30), y_test: (85443,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"TRAINING: X_train: {X_train.shape}, y_train: {y_train.shape}\\n{'_'*55}\")\n",
    "print(f\"VALIDATION: X_validate: {X_validate.shape}, y_validate: {y_validate.shape}\\n{'_'*50}\")\n",
    "print(f\"TESTING: X_test: {X_test.shape}, y_test: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, f1_score\n",
    "\n",
    "def print_score(label, prediction, train=True):\n",
    "    if train:\n",
    "        clf_report = pd.DataFrame(classification_report(label, prediction, output_dict=True))\n",
    "        print(\"Train Result:\\n================================================\")\n",
    "        print(f\"Accuracy Score: {accuracy_score(label, prediction) * 100:.2f}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Classification Report:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(y_train, prediction)}\\n\")\n",
    "        \n",
    "    elif train==False:\n",
    "        clf_report = pd.DataFrame(classification_report(label, prediction, output_dict=True))\n",
    "        print(\"Test Result:\\n================================================\")        \n",
    "        print(f\"Accuracy Score: {accuracy_score(label, prediction) * 100:.2f}%\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Classification Report:\\n{clf_report}\")\n",
    "        print(\"_______________________________________________\")\n",
    "        print(f\"Confusion Matrix: \\n {confusion_matrix(label, prediction)}\\n\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 256)               7936      \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64)                256       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 50,945\n",
      "Trainable params: 50,049\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(256, activation='relu', input_shape=(X_train.shape[-1],)),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(64, activation='relu'),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(0.3),\n",
    "    keras.layers.Dense(1, activation='sigmoid'),\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "78/78 [==============================] - 6s 44ms/step - loss: 0.7367 - fn: 37.7595 - fp: 26847.7342 - tn: 54894.4304 - tp: 107.7468 - precision: 0.0034 - recall: 0.6719 - val_loss: 0.3069 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 2/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.3265 - fn: 31.4304 - fp: 2225.2658 - tn: 79514.7468 - tp: 116.2278 - precision: 0.0456 - recall: 0.7846 - val_loss: 0.1294 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 3/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.1260 - fn: 41.2532 - fp: 336.7089 - tn: 81407.4430 - tp: 102.2658 - precision: 0.2194 - recall: 0.7239 - val_loss: 0.0555 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 4/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0574 - fn: 42.7215 - fp: 123.5696 - tn: 81614.0506 - tp: 107.3291 - precision: 0.4831 - recall: 0.7156 - val_loss: 0.0334 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 5/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0388 - fn: 46.4557 - fp: 69.2278 - tn: 81671.1392 - tp: 100.8481 - precision: 0.5442 - recall: 0.6752 - val_loss: 0.0249 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 6/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0254 - fn: 46.7468 - fp: 54.3038 - tn: 81685.7595 - tp: 100.8608 - precision: 0.6944 - recall: 0.7095 - val_loss: 0.0209 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 7/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0213 - fn: 42.6456 - fp: 30.5316 - tn: 81708.4051 - tp: 106.0886 - precision: 0.7563 - recall: 0.7234 - val_loss: 0.0174 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 8/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0209 - fn: 52.6456 - fp: 40.1772 - tn: 81699.7975 - tp: 95.0506 - precision: 0.7090 - recall: 0.6291 - val_loss: 0.0200 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 9/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0204 - fn: 42.6582 - fp: 29.3291 - tn: 81716.7975 - tp: 98.8861 - precision: 0.7725 - recall: 0.7265 - val_loss: 0.0127 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 10/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 0.0165 - fn: 48.5063 - fp: 34.0759 - tn: 81707.2405 - tp: 97.8481 - precision: 0.7400 - recall: 0.6704 - val_loss: 0.0135 - val_fn: 14.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 55.0000 - val_precision: 0.7971 - val_recall: 0.7971\n",
      "Epoch 11/300\n",
      "78/78 [==============================] - 3s 41ms/step - loss: 0.0137 - fn: 46.6835 - fp: 23.0506 - tn: 81716.7975 - tp: 101.1392 - precision: 0.8221 - recall: 0.6977 - val_loss: 0.0158 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 12/300\n",
      "78/78 [==============================] - 6s 80ms/step - loss: 0.0153 - fn: 48.0380 - fp: 27.5443 - tn: 81713.0127 - tp: 99.0759 - precision: 0.7428 - recall: 0.6467 - val_loss: 0.0080 - val_fn: 13.0000 - val_fp: 13.0000 - val_tn: 39791.0000 - val_tp: 56.0000 - val_precision: 0.8116 - val_recall: 0.8116\n",
      "Epoch 13/300\n",
      "78/78 [==============================] - 4s 57ms/step - loss: 0.0136 - fn: 54.2658 - fp: 21.5570 - tn: 81715.6203 - tp: 96.2278 - precision: 0.8344 - recall: 0.6408 - val_loss: 0.0181 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 14/300\n",
      "78/78 [==============================] - 4s 49ms/step - loss: 0.0123 - fn: 45.8987 - fp: 28.1899 - tn: 81702.0633 - tp: 111.5190 - precision: 0.8080 - recall: 0.7306 - val_loss: 0.0095 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 15/300\n",
      "78/78 [==============================] - 3s 43ms/step - loss: 0.0103 - fn: 45.4430 - fp: 17.0000 - tn: 81724.4051 - tp: 100.8228 - precision: 0.8683 - recall: 0.6825 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 14.0000 - val_tn: 39790.0000 - val_tp: 56.0000 - val_precision: 0.8000 - val_recall: 0.8116\n",
      "Epoch 16/300\n",
      "78/78 [==============================] - 3s 42ms/step - loss: 0.0114 - fn: 50.9620 - fp: 19.6582 - tn: 81715.1646 - tp: 101.8861 - precision: 0.8565 - recall: 0.6528 - val_loss: 0.0053 - val_fn: 14.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 55.0000 - val_precision: 0.8333 - val_recall: 0.7971\n",
      "Epoch 17/300\n",
      "78/78 [==============================] - 3s 41ms/step - loss: 0.0094 - fn: 44.0633 - fp: 21.0000 - tn: 81725.3924 - tp: 97.2152 - precision: 0.8311 - recall: 0.6716 - val_loss: 0.0078 - val_fn: 13.0000 - val_fp: 13.0000 - val_tn: 39791.0000 - val_tp: 56.0000 - val_precision: 0.8116 - val_recall: 0.8116\n",
      "Epoch 18/300\n",
      "78/78 [==============================] - 3s 40ms/step - loss: 0.0086 - fn: 38.5443 - fp: 14.2025 - tn: 81731.8861 - tp: 103.0380 - precision: 0.8868 - recall: 0.7144 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 55.0000 - val_precision: 0.8209 - val_recall: 0.7971\n",
      "Epoch 19/300\n",
      "78/78 [==============================] - 3s 38ms/step - loss: 0.0067 - fn: 47.9367 - fp: 19.2785 - tn: 81723.6203 - tp: 96.8354 - precision: 0.8406 - recall: 0.6957 - val_loss: 0.0054 - val_fn: 32.0000 - val_fp: 1.0000 - val_tn: 39803.0000 - val_tp: 37.0000 - val_precision: 0.9737 - val_recall: 0.5362\n",
      "Epoch 20/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0054 - fn: 47.3671 - fp: 11.9873 - tn: 81736.1646 - tp: 92.1519 - precision: 0.9034 - recall: 0.6554 - val_loss: 0.0057 - val_fn: 14.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 55.0000 - val_precision: 0.8209 - val_recall: 0.7971\n",
      "Epoch 21/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 0.0056 - fn: 47.0253 - fp: 15.1899 - tn: 81719.9873 - tp: 105.4684 - precision: 0.9005 - recall: 0.7094 - val_loss: 0.0045 - val_fn: 18.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 51.0000 - val_precision: 0.8361 - val_recall: 0.7391\n",
      "Epoch 22/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0054 - fn: 51.8101 - fp: 17.6076 - tn: 81722.8481 - tp: 95.4051 - precision: 0.8375 - recall: 0.6405 - val_loss: 0.0053 - val_fn: 14.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 55.0000 - val_precision: 0.8333 - val_recall: 0.7971\n",
      "Epoch 23/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0045 - fn: 45.9873 - fp: 14.3038 - tn: 81721.9747 - tp: 105.4051 - precision: 0.8992 - recall: 0.7108 - val_loss: 0.0047 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 24/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0044 - fn: 48.7215 - fp: 13.1899 - tn: 81725.8354 - tp: 99.9241 - precision: 0.8883 - recall: 0.6731 - val_loss: 0.0042 - val_fn: 23.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 46.0000 - val_precision: 0.9020 - val_recall: 0.6667\n",
      "Epoch 25/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0041 - fn: 51.0253 - fp: 17.0759 - tn: 81723.0253 - tp: 96.5443 - precision: 0.8734 - recall: 0.6455 - val_loss: 0.0043 - val_fn: 17.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 52.0000 - val_precision: 0.8667 - val_recall: 0.7536\n",
      "Epoch 26/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0036 - fn: 42.9747 - fp: 12.2785 - tn: 81735.5063 - tp: 96.9114 - precision: 0.8966 - recall: 0.6995 - val_loss: 0.0042 - val_fn: 18.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 51.0000 - val_precision: 0.9273 - val_recall: 0.7391\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/300\n",
      "78/78 [==============================] - 3s 37ms/step - loss: 0.0041 - fn: 43.2532 - fp: 13.6835 - tn: 81728.7848 - tp: 101.9494 - precision: 0.8669 - recall: 0.6984 - val_loss: 0.0042 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 28/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 0.0037 - fn: 37.2278 - fp: 12.5823 - tn: 81730.4810 - tp: 107.3797 - precision: 0.8888 - recall: 0.7572 - val_loss: 0.0042 - val_fn: 18.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 51.0000 - val_precision: 0.8947 - val_recall: 0.7391\n",
      "Epoch 29/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0035 - fn: 42.2152 - fp: 14.7722 - tn: 81729.8481 - tp: 100.8354 - precision: 0.8745 - recall: 0.7037 - val_loss: 0.0044 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 30/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 0.0044 - fn: 46.0000 - fp: 11.6076 - tn: 81724.9367 - tp: 105.1266 - precision: 0.9008 - recall: 0.6733 - val_loss: 0.0045 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 31/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0041 - fn: 48.1392 - fp: 14.1013 - tn: 81717.2405 - tp: 108.1899 - precision: 0.8921 - recall: 0.6943 - val_loss: 0.0044 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 32/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0032 - fn: 39.6709 - fp: 11.3165 - tn: 81721.1646 - tp: 115.5190 - precision: 0.9061 - recall: 0.7586 - val_loss: 0.0043 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 33/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0032 - fn: 39.2532 - fp: 13.5316 - tn: 81715.4177 - tp: 119.4684 - precision: 0.8923 - recall: 0.7744 - val_loss: 0.0044 - val_fn: 21.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 48.0000 - val_precision: 0.8889 - val_recall: 0.6957\n",
      "Epoch 34/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 0.0033 - fn: 42.3418 - fp: 11.9494 - tn: 81719.7722 - tp: 113.6076 - precision: 0.9062 - recall: 0.7266 - val_loss: 0.0046 - val_fn: 24.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 45.0000 - val_precision: 0.9184 - val_recall: 0.6522\n",
      "Epoch 35/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0032 - fn: 39.6582 - fp: 12.4304 - tn: 81725.8861 - tp: 109.6962 - precision: 0.8713 - recall: 0.7067 - val_loss: 0.0041 - val_fn: 18.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 51.0000 - val_precision: 0.8947 - val_recall: 0.7391\n",
      "Epoch 36/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0031 - fn: 40.4810 - fp: 14.3418 - tn: 81725.9494 - tp: 106.8987 - precision: 0.8768 - recall: 0.7250 - val_loss: 0.0044 - val_fn: 18.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 51.0000 - val_precision: 0.8947 - val_recall: 0.7391\n",
      "Epoch 37/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0025 - fn: 37.0886 - fp: 9.9873 - tn: 81743.2785 - tp: 97.3165 - precision: 0.9146 - recall: 0.7022 - val_loss: 0.0045 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 38/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0025 - fn: 34.1899 - fp: 9.4304 - tn: 81733.1899 - tp: 110.8608 - precision: 0.9308 - recall: 0.7714 - val_loss: 0.0045 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 39/300\n",
      "78/78 [==============================] - 3s 37ms/step - loss: 0.0032 - fn: 40.7468 - fp: 14.0127 - tn: 81731.6329 - tp: 101.2785 - precision: 0.8767 - recall: 0.7071 - val_loss: 0.0045 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 40/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0029 - fn: 36.1899 - fp: 8.4430 - tn: 81728.5570 - tp: 114.4810 - precision: 0.9366 - recall: 0.7420 - val_loss: 0.0048 - val_fn: 27.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 42.0000 - val_precision: 0.8936 - val_recall: 0.6087\n",
      "Epoch 41/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0032 - fn: 39.9367 - fp: 11.1646 - tn: 81723.7342 - tp: 112.8354 - precision: 0.9206 - recall: 0.7227 - val_loss: 0.0046 - val_fn: 19.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 50.0000 - val_precision: 0.8929 - val_recall: 0.7246\n",
      "Epoch 42/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0024 - fn: 36.4304 - fp: 11.6329 - tn: 81731.8734 - tp: 107.7342 - precision: 0.9071 - recall: 0.7509 - val_loss: 0.0048 - val_fn: 18.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 51.0000 - val_precision: 0.8947 - val_recall: 0.7391\n",
      "Epoch 43/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0023 - fn: 33.1646 - fp: 8.9620 - tn: 81730.9494 - tp: 114.5949 - precision: 0.9171 - recall: 0.7872 - val_loss: 0.0044 - val_fn: 15.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 54.0000 - val_precision: 0.8852 - val_recall: 0.7826\n",
      "Epoch 44/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0030 - fn: 37.1899 - fp: 13.0506 - tn: 81714.2658 - tp: 123.1646 - precision: 0.8903 - recall: 0.7610 - val_loss: 0.0048 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 45/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0021 - fn: 31.5696 - fp: 10.4177 - tn: 81729.3418 - tp: 116.3418 - precision: 0.9232 - recall: 0.8061 - val_loss: 0.0050 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 46/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 0.0025 - fn: 33.3418 - fp: 8.3924 - tn: 81729.9241 - tp: 116.0127 - precision: 0.9394 - recall: 0.7805 - val_loss: 0.0045 - val_fn: 18.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 51.0000 - val_precision: 0.9107 - val_recall: 0.7391\n",
      "Epoch 47/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0021 - fn: 32.9873 - fp: 10.4430 - tn: 81724.1139 - tp: 120.1266 - precision: 0.9298 - recall: 0.8073 - val_loss: 0.0045 - val_fn: 20.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 49.0000 - val_precision: 0.9074 - val_recall: 0.7101\n",
      "Epoch 48/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0030 - fn: 38.6329 - fp: 10.7215 - tn: 81728.8861 - tp: 109.4304 - precision: 0.9168 - recall: 0.7038 - val_loss: 0.0047 - val_fn: 21.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 48.0000 - val_precision: 0.9057 - val_recall: 0.6957\n",
      "Epoch 49/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0024 - fn: 30.0253 - fp: 7.8734 - tn: 81725.4557 - tp: 124.3165 - precision: 0.9497 - recall: 0.8095 - val_loss: 0.0046 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 50/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0017 - fn: 25.1772 - fp: 7.6709 - tn: 81736.5443 - tp: 118.2785 - precision: 0.9454 - recall: 0.8211 - val_loss: 0.0046 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 51/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 0.0021 - fn: 28.0633 - fp: 10.9494 - tn: 81739.4937 - tp: 109.1646 - precision: 0.9126 - recall: 0.7752 - val_loss: 0.0046 - val_fn: 13.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 56.0000 - val_precision: 0.9180 - val_recall: 0.8116\n",
      "Epoch 52/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0022 - fn: 32.6456 - fp: 10.7468 - tn: 81728.8861 - tp: 115.3924 - precision: 0.9027 - recall: 0.7558 - val_loss: 0.0049 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 53/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0017 - fn: 29.4177 - fp: 9.2025 - tn: 81733.5190 - tp: 115.5316 - precision: 0.9303 - recall: 0.8073 - val_loss: 0.0049 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 54/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0021 - fn: 29.1646 - fp: 13.8734 - tn: 81733.3291 - tp: 111.3038 - precision: 0.8981 - recall: 0.8093 - val_loss: 0.0048 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 55/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0018 - fn: 28.3797 - fp: 10.7595 - tn: 81725.5190 - tp: 123.0127 - precision: 0.9158 - recall: 0.8252 - val_loss: 0.0052 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 56/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0017 - fn: 24.8734 - fp: 6.6709 - tn: 81728.0759 - tp: 128.0506 - precision: 0.9490 - recall: 0.8534 - val_loss: 0.0051 - val_fn: 22.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 47.0000 - val_precision: 0.8868 - val_recall: 0.6812\n",
      "Epoch 57/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0022 - fn: 32.8228 - fp: 12.5190 - tn: 81719.3291 - tp: 123.0000 - precision: 0.8989 - recall: 0.7703 - val_loss: 0.0049 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n",
      "Epoch 58/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0020 - fn: 28.2152 - fp: 7.0380 - tn: 81739.7722 - tp: 112.6456 - precision: 0.9413 - recall: 0.7845 - val_loss: 0.0049 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 59/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0020 - fn: 31.0886 - fp: 10.8734 - tn: 81724.8734 - tp: 120.8354 - precision: 0.9122 - recall: 0.7738 - val_loss: 0.0049 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n",
      "Epoch 60/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0022 - fn: 30.9494 - fp: 9.4937 - tn: 81727.3797 - tp: 119.8481 - precision: 0.9394 - recall: 0.7934 - val_loss: 0.0047 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 61/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 0.0023 - fn: 28.1139 - fp: 10.4177 - tn: 81725.8987 - tp: 123.2405 - precision: 0.9026 - recall: 0.8096 - val_loss: 0.0047 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 62/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0019 - fn: 27.1013 - fp: 8.8734 - tn: 81733.3038 - tp: 118.3924 - precision: 0.9247 - recall: 0.8350 - val_loss: 0.0047 - val_fn: 14.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 55.0000 - val_precision: 0.9167 - val_recall: 0.7971\n",
      "Epoch 63/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0019 - fn: 25.8354 - fp: 9.0127 - tn: 81723.4304 - tp: 129.3924 - precision: 0.9490 - recall: 0.8377 - val_loss: 0.0048 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.81169 - fp: 7.7042 - tn: 73579.0986 - tp: 118.2113 - precision: 0.9526 - recall: 0.\n",
      "Epoch 64/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0019 - fn: 27.6456 - fp: 9.6203 - tn: 81731.5063 - tp: 118.8987 - precision: 0.9277 - recall: 0.8128 - val_loss: 0.0048 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 65/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0018 - fn: 28.2152 - fp: 11.0000 - tn: 81732.2532 - tp: 116.2025 - precision: 0.9155 - recall: 0.8123 - val_loss: 0.0047 - val_fn: 17.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 52.0000 - val_precision: 0.8966 - val_recall: 0.7536\n",
      "Epoch 66/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0013 - fn: 21.1139 - fp: 8.0380 - tn: 81739.5063 - tp: 119.0127 - precision: 0.9428 - recall: 0.8746 - val_loss: 0.0049 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 67/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0016 - fn: 23.9620 - fp: 8.1519 - tn: 81734.3544 - tp: 121.2025 - precision: 0.9262 - recall: 0.8342 - val_loss: 0.0048 - val_fn: 14.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 55.0000 - val_precision: 0.9167 - val_recall: 0.7971\n",
      "Epoch 68/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0020 - fn: 25.6962 - fp: 9.1392 - tn: 81727.2785 - tp: 125.5570 - precision: 0.9390 - recall: 0.8059 - val_loss: 0.0050 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 69/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0019 - fn: 31.0253 - fp: 10.0253 - tn: 81730.5823 - tp: 116.0380 - precision: 0.9121 - recall: 0.7734 - val_loss: 0.0050 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 70/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0020 - fn: 25.7595 - fp: 13.2785 - tn: 81724.3797 - tp: 124.2532 - precision: 0.9002 - recall: 0.8314 - val_loss: 0.0050 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 71/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0014 - fn: 22.1139 - fp: 8.5570 - tn: 81733.5190 - tp: 123.4810 - precision: 0.9284 - recall: 0.8521 - val_loss: 0.0052 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 72/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0013 - fn: 21.2532 - fp: 7.7342 - tn: 81733.6456 - tp: 125.0380 - precision: 0.9420 - recall: 0.8738 - val_loss: 0.0053 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 73/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0015 - fn: 20.9367 - fp: 6.8734 - tn: 81732.0000 - tp: 127.8608 - precision: 0.9550 - recall: 0.8588 - val_loss: 0.0050 - val_fn: 16.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 53.0000 - val_precision: 0.8833 - val_recall: 0.7681\n",
      "Epoch 74/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0015 - fn: 22.0633 - fp: 10.5696 - tn: 81724.4557 - tp: 130.5823 - precision: 0.9286 - recall: 0.8685 - val_loss: 0.0052 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 75/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0018 - fn: 24.7595 - fp: 11.1899 - tn: 81723.9873 - tp: 127.7342 - precision: 0.9049 - recall: 0.8382 - val_loss: 0.0052 - val_fn: 18.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 51.0000 - val_precision: 0.8947 - val_recall: 0.7391\n",
      "Epoch 76/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0017 - fn: 27.2152 - fp: 8.2785 - tn: 81730.5316 - tp: 121.6456 - precision: 0.9243 - recall: 0.7954 - val_loss: 0.0052 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 77/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0013 - fn: 21.6709 - fp: 5.5190 - tn: 81736.7975 - tp: 123.6835 - precision: 0.9658 - recall: 0.8556 - val_loss: 0.0049 - val_fn: 13.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 56.0000 - val_precision: 0.9032 - val_recall: 0.8116\n",
      "Epoch 78/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0017 - fn: 26.0886 - fp: 6.5316 - tn: 81730.2532 - tp: 124.7975 - precision: 0.9513 - recall: 0.8210 - val_loss: 0.0049 - val_fn: 15.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 54.0000 - val_precision: 0.9153 - val_recall: 0.7826\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 79/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0011 - fn: 21.5570 - fp: 5.0506 - tn: 81748.7215 - tp: 112.3418 - precision: 0.9618 - recall: 0.8399 - val_loss: 0.0049 - val_fn: 14.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 55.0000 - val_precision: 0.9167 - val_recall: 0.7971\n",
      "Epoch 80/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0012 - fn: 18.9873 - fp: 5.9873 - tn: 81729.6076 - tp: 133.0886 - precision: 0.9603 - recall: 0.8746 - val_loss: 0.0051 - val_fn: 18.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 51.0000 - val_precision: 0.9107 - val_recall: 0.7391\n",
      "Epoch 81/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0015 - fn: 22.1266 - fp: 10.1392 - tn: 81730.7215 - tp: 124.6835 - precision: 0.9340 - recall: 0.8451 - val_loss: 0.0049 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n",
      "Epoch 82/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0014 - fn: 22.8354 - fp: 7.8987 - tn: 81738.6203 - tp: 118.3165 - precision: 0.9438 - recall: 0.8417 - val_loss: 0.0054 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 83/300\n",
      "78/78 [==============================] - ETA: 0s - loss: 0.0016 - fn: 25.2987 - fp: 9.5714 - tn: 79715.1818 - tp: 121.9481 - precision: 0.9321 - recall: 0.842 - 2s 29ms/step - loss: 0.0016 - fn: 25.8987 - fp: 9.8101 - tn: 81727.0759 - tp: 124.8861 - precision: 0.9319 - recall: 0.8420 - val_loss: 0.0054 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 84/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0019 - fn: 26.9494 - fp: 8.0759 - tn: 81735.3418 - tp: 117.3038 - precision: 0.9427 - recall: 0.8052 - val_loss: 0.0053 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 85/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0016 - fn: 22.3165 - fp: 8.2658 - tn: 81729.3418 - tp: 127.7468 - precision: 0.9477 - recall: 0.8406 - val_loss: 0.0048 - val_fn: 17.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 52.0000 - val_precision: 0.8966 - val_recall: 0.7536\n",
      "Epoch 86/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0015 - fn: 20.6582 - fp: 8.5190 - tn: 81729.7722 - tp: 128.7215 - precision: 0.9424 - recall: 0.8572 - val_loss: 0.0047 - val_fn: 17.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 52.0000 - val_precision: 0.9286 - val_recall: 0.7536\n",
      "Epoch 87/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0017 - fn: 27.4684 - fp: 7.6835 - tn: 81728.6456 - tp: 123.8734 - precision: 0.9452 - recall: 0.8148 - val_loss: 0.0049 - val_fn: 13.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 56.0000 - val_precision: 0.9180 - val_recall: 0.8116\n",
      "Epoch 88/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0011 - fn: 19.4051 - fp: 7.9747 - tn: 81731.3797 - tp: 128.9114 - precision: 0.9333 - recall: 0.8559 - val_loss: 0.0052 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 89/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 0.0011 - fn: 16.4810 - fp: 7.2911 - tn: 81730.2911 - tp: 133.6076 - precision: 0.9475 - recall: 0.8918 - val_loss: 0.0051 - val_fn: 19.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 50.0000 - val_precision: 0.8772 - val_recall: 0.7246\n",
      "Epoch 90/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 0.0014 - fn: 23.4304 - fp: 10.4430 - tn: 81725.1013 - tp: 128.6962 - precision: 0.9146 - recall: 0.8331 - val_loss: 0.0051 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 91/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 9.5696e-04 - fn: 17.6203 - fp: 5.7595 - tn: 81736.7722 - tp: 127.5190 - precision: 0.9614 - recall: 0.8627 - val_loss: 0.0051 - val_fn: 17.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 52.0000 - val_precision: 0.9123 - val_recall: 0.7536\n",
      "Epoch 92/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 0.0012 - fn: 20.2532 - fp: 6.2532 - tn: 81728.6203 - tp: 132.5443 - precision: 0.9626 - recall: 0.8668 - val_loss: 0.0051 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 93/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 0.0013 - fn: 20.1519 - fp: 8.4051 - tn: 81722.9494 - tp: 136.1646 - precision: 0.9451 - recall: 0.8803 - val_loss: 0.0052 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 94/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 0.0015 - fn: 20.8734 - fp: 8.9367 - tn: 81730.3797 - tp: 127.4810 - precision: 0.9288 - recall: 0.8655 - val_loss: 0.0050 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 95/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 0.0011 - fn: 16.5063 - fp: 5.8861 - tn: 81732.5316 - tp: 132.7468 - precision: 0.9439 - recall: 0.8513 - val_loss: 0.0052 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 96/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 6.6354e-04 - fn: 13.6076 - fp: 3.4430 - tn: 81732.6203 - tp: 138.0000 - precision: 0.9817 - recall: 0.9183 - val_loss: 0.0052 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n",
      "Epoch 97/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 8.9841e-04 - fn: 17.2278 - fp: 8.0506 - tn: 81736.6709 - tp: 125.7215 - precision: 0.9332 - recall: 0.8847 - val_loss: 0.0056 - val_fn: 16.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 53.0000 - val_precision: 0.8833 - val_recall: 0.7681\n",
      "Epoch 98/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 8.9060e-04 - fn: 15.6582 - fp: 5.9873 - tn: 81730.4937 - tp: 135.5316 - precision: 0.9581 - recall: 0.9121 - val_loss: 0.0054 - val_fn: 17.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 52.0000 - val_precision: 0.8966 - val_recall: 0.7536\n",
      "Epoch 99/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0011 - fn: 19.8101 - fp: 7.2278 - tn: 81740.1013 - tp: 120.5316 - precision: 0.9560 - recall: 0.8531 - val_loss: 0.0054 - val_fn: 16.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 53.0000 - val_precision: 0.8833 - val_recall: 0.7681\n",
      "Epoch 100/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0011 - fn: 19.8481 - fp: 5.7215 - tn: 81728.5316 - tp: 133.5696 - precision: 0.9681 - recall: 0.8633 - val_loss: 0.0050 - val_fn: 13.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 56.0000 - val_precision: 0.9180 - val_recall: 0.8116\n",
      "Epoch 101/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0011 - fn: 19.2405 - fp: 6.9367 - tn: 81733.8481 - tp: 127.6456 - precision: 0.9509 - recall: 0.8806 - val_loss: 0.0054 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n",
      "Epoch 102/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 9.9500e-04 - fn: 17.4557 - fp: 4.8734 - tn: 81734.4937 - tp: 130.8481 - precision: 0.9697 - recall: 0.8880 - val_loss: 0.0057 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 103/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 9.8176e-04 - fn: 17.6203 - fp: 9.2152 - tn: 81730.8481 - tp: 129.9873 - precision: 0.9352 - recall: 0.8752 - val_loss: 0.0052 - val_fn: 17.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 52.0000 - val_precision: 0.8966 - val_recall: 0.7536\n",
      "Epoch 104/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0010 - fn: 18.9241 - fp: 7.3291 - tn: 81732.4937 - tp: 128.9241 - precision: 0.9584 - recall: 0.8702 - val_loss: 0.0054 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 105/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0011 - fn: 15.7975 - fp: 8.1266 - tn: 81730.6709 - tp: 133.0759 - precision: 0.9465 - recall: 0.9001 - val_loss: 0.0056 - val_fn: 17.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 52.0000 - val_precision: 0.8966 - val_recall: 0.7536\n",
      "Epoch 106/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 8.2652e-04 - fn: 14.8861 - fp: 7.0127 - tn: 81734.1646 - tp: 131.6076 - precision: 0.9480 - recall: 0.9052 - val_loss: 0.0057 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 107/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.9776e-04 - fn: 12.1646 - fp: 3.5696 - tn: 81738.9241 - tp: 133.0127 - precision: 0.9717 - recall: 0.9293 - val_loss: 0.0058 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 108/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0011 - fn: 17.0886 - fp: 8.3038 - tn: 81723.5949 - tp: 138.6835 - precision: 0.9437 - recall: 0.8961 - val_loss: 0.0060 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 109/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0010 - fn: 16.0127 - fp: 5.4937 - tn: 81734.0633 - tp: 132.1013 - precision: 0.9586 - recall: 0.8788 - val_loss: 0.0059 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 110/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 8.6293e-04 - fn: 13.6835 - fp: 8.2532 - tn: 81733.7848 - tp: 131.9494 - precision: 0.9421 - recall: 0.9216 - val_loss: 0.0055 - val_fn: 17.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 52.0000 - val_precision: 0.9123 - val_recall: 0.7536\n",
      "Epoch 111/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 8.6551e-04 - fn: 14.3038 - fp: 4.8734 - tn: 81741.2025 - tp: 127.2911 - precision: 0.9729 - recall: 0.9034 - val_loss: 0.0057 - val_fn: 17.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 52.0000 - val_precision: 0.9123 - val_recall: 0.7536\n",
      "Epoch 112/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0010 - fn: 18.6835 - fp: 7.2405 - tn: 81733.5823 - tp: 128.1646 - precision: 0.9548 - recall: 0.8465 - val_loss: 0.0057 - val_fn: 15.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 54.0000 - val_precision: 0.8852 - val_recall: 0.7826\n",
      "Epoch 113/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0011 - fn: 16.5063 - fp: 7.4937 - tn: 81735.2911 - tp: 128.3797 - precision: 0.9405 - recall: 0.8869 - val_loss: 0.0058 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 114/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 9.3795e-04 - fn: 16.7848 - fp: 6.3291 - tn: 81729.2532 - tp: 135.3038 - precision: 0.9505 - recall: 0.8941 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 115/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 7.6475e-04 - fn: 16.8101 - fp: 6.7722 - tn: 81730.7722 - tp: 133.3165 - precision: 0.9628 - recall: 0.8853 - val_loss: 0.0057 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 116/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0012 - fn: 16.6456 - fp: 10.2911 - tn: 81733.0127 - tp: 127.7215 - precision: 0.9101 - recall: 0.8811 - val_loss: 0.0061 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 117/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 0.0010 - fn: 16.0127 - fp: 6.5316 - tn: 81735.8101 - tp: 129.3165 - precision: 0.9455 - recall: 0.8886 - val_loss: 0.0062 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 118/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 8.6843e-04 - fn: 13.7975 - fp: 8.6076 - tn: 81717.1899 - tp: 148.0759 - precision: 0.9473 - recall: 0.9131 - val_loss: 0.0062 - val_fn: 22.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 47.0000 - val_precision: 0.9216 - val_recall: 0.6812\n",
      "Epoch 119/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0010 - fn: 20.3291 - fp: 4.9747 - tn: 81736.5190 - tp: 125.8481 - precision: 0.9718 - recall: 0.8504 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 120/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 6.8588e-04 - fn: 14.4684 - fp: 6.2405 - tn: 81733.1772 - tp: 133.7848 - precision: 0.9566 - recall: 0.9037 - val_loss: 0.0060 - val_fn: 16.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 53.0000 - val_precision: 0.9138 - val_recall: 0.7681\n",
      "Epoch 121/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 0.0010 - fn: 18.7089 - fp: 8.3291 - tn: 81730.9114 - tp: 129.7215 - precision: 0.9450 - recall: 0.8501 - val_loss: 0.0056 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 122/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 9.1209e-04 - fn: 13.2911 - fp: 7.7342 - tn: 81736.4684 - tp: 130.1772 - precision: 0.9353 - recall: 0.9147 - val_loss: 0.0060 - val_fn: 17.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 52.0000 - val_precision: 0.8525 - val_recall: 0.7536\n",
      "Epoch 123/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 8.5462e-04 - fn: 12.2405 - fp: 6.4304 - tn: 81745.5316 - tp: 123.4684 - precision: 0.9446 - recall: 0.9118 - val_loss: 0.0062 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 124/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 8.3515e-04 - fn: 12.7595 - fp: 5.6076 - tn: 81729.2911 - tp: 140.0127 - precision: 0.9588 - recall: 0.9136 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 125/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 7.0462e-04 - fn: 12.2405 - fp: 6.6076 - tn: 81723.9241 - tp: 144.8987 - precision: 0.9564 - recall: 0.9337 - val_loss: 0.0062 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 126/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 8.0134e-04 - fn: 11.8987 - fp: 7.7468 - tn: 81722.9494 - tp: 145.0759 - precision: 0.9194 - recall: 0.9072 - val_loss: 0.0061 - val_fn: 17.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 52.0000 - val_precision: 0.9123 - val_recall: 0.7536\n",
      "Epoch 127/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 8.1746e-04 - fn: 12.5823 - fp: 5.9114 - tn: 81730.5316 - tp: 138.6456 - precision: 0.9637 - recall: 0.8969 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 128/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 6.6979e-04 - fn: 13.3291 - fp: 4.5949 - tn: 81742.8354 - tp: 126.9114 - precision: 0.9699 - recall: 0.8944 - val_loss: 0.0058 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 129/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 3s 35ms/step - loss: 9.3857e-04 - fn: 13.9494 - fp: 6.5316 - tn: 81728.6835 - tp: 138.5063 - precision: 0.9495 - recall: 0.9070 - val_loss: 0.0064 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 130/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 5.9967e-04 - fn: 12.1772 - fp: 5.9114 - tn: 81729.1392 - tp: 140.4430 - precision: 0.9537 - recall: 0.9275 - val_loss: 0.0062 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 131/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 8.2521e-04 - fn: 11.2658 - fp: 6.9747 - tn: 81740.6076 - tp: 128.8228 - precision: 0.9416 - recall: 0.9164 - val_loss: 0.0061 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 132/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 8.6912e-04 - fn: 12.6456 - fp: 8.5823 - tn: 81726.8354 - tp: 139.6076 - precision: 0.9394 - recall: 0.9164 - val_loss: 0.0058 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 133/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 8.9775e-04 - fn: 14.0380 - fp: 5.3418 - tn: 81726.1392 - tp: 142.1519 - precision: 0.9708 - recall: 0.9027 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 134/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 5.8042e-04 - fn: 10.3418 - fp: 4.8734 - tn: 81735.2532 - tp: 137.2025 - precision: 0.9750 - recall: 0.9415 - val_loss: 0.0064 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 135/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 6.8864e-04 - fn: 12.3291 - fp: 6.1139 - tn: 81739.4304 - tp: 129.7975 - precision: 0.9451 - recall: 0.9330 - val_loss: 0.0063 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 136/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 6.3439e-04 - fn: 10.8987 - fp: 5.9114 - tn: 81730.6076 - tp: 140.2532 - precision: 0.9544 - recall: 0.9326 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 137/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 8.5388e-04 - fn: 12.7089 - fp: 9.5570 - tn: 81720.2658 - tp: 145.1392 - precision: 0.9369 - recall: 0.9284 - val_loss: 0.0061 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 138/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 5.6101e-04 - fn: 10.1392 - fp: 2.0000 - tn: 81746.7975 - tp: 128.7342 - precision: 0.9904 - recall: 0.9254 - val_loss: 0.0061 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 139/300\n",
      "78/78 [==============================] - 3s 37ms/step - loss: 8.5097e-04 - fn: 11.5949 - fp: 5.5823 - tn: 81739.0633 - tp: 131.4304 - precision: 0.9535 - recall: 0.9112 - val_loss: 0.0062 - val_fn: 16.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 53.0000 - val_precision: 0.8689 - val_recall: 0.7681\n",
      "Epoch 140/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 7.0414e-04 - fn: 12.1646 - fp: 4.3924 - tn: 81742.8481 - tp: 128.2658 - precision: 0.9688 - recall: 0.8973 - val_loss: 0.0060 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 141/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 6.3416e-04 - fn: 10.6203 - fp: 5.8608 - tn: 81734.9620 - tp: 136.2278 - precision: 0.9528 - recall: 0.9314 - val_loss: 0.0064 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 142/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 4.0776e-04 - fn: 8.3797 - fp: 2.5823 - tn: 81737.6076 - tp: 139.1013 - precision: 0.9873 - recall: 0.9533 - val_loss: 0.0062 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 143/300\n",
      "78/78 [==============================] - 3s 37ms/step - loss: 5.5275e-04 - fn: 11.9747 - fp: 7.0633 - tn: 81733.9114 - tp: 134.7215 - precision: 0.9329 - recall: 0.9134 - val_loss: 0.0064 - val_fn: 15.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 54.0000 - val_precision: 0.8571 - val_recall: 0.7826\n",
      "Epoch 144/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 7.9736e-04 - fn: 14.5823 - fp: 7.1392 - tn: 81726.3924 - tp: 139.5570 - precision: 0.9431 - recall: 0.9002 - val_loss: 0.0064 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 145/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 7.6253e-04 - fn: 12.3165 - fp: 5.7468 - tn: 81738.1899 - tp: 131.4177 - precision: 0.9653 - recall: 0.9131 - val_loss: 0.0061 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 146/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.8686e-04 - fn: 11.7342 - fp: 4.3924 - tn: 81737.1013 - tp: 134.4430 - precision: 0.9677 - recall: 0.9241 - val_loss: 0.0065 - val_fn: 12.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 57.0000 - val_precision: 0.8507 - val_recall: 0.8261\n",
      "Epoch 147/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.2442e-04 - fn: 8.7848 - fp: 4.6962 - tn: 81743.2532 - tp: 130.9367 - precision: 0.9758 - recall: 0.9462 - val_loss: 0.0065 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 148/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 7.4899e-04 - fn: 13.0506 - fp: 3.4684 - tn: 81734.0759 - tp: 137.0759 - precision: 0.9812 - recall: 0.9161 - val_loss: 0.0062 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 149/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 9.4285e-04 - fn: 13.5823 - fp: 6.7975 - tn: 81735.8228 - tp: 131.4684 - precision: 0.9327 - recall: 0.8978 - val_loss: 0.0061 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 150/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.8019e-04 - fn: 9.3291 - fp: 4.4430 - tn: 81734.0127 - tp: 139.8861 - precision: 0.9770 - recall: 0.9464 - val_loss: 0.0061 - val_fn: 12.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 57.0000 - val_precision: 0.8906 - val_recall: 0.8261\n",
      "Epoch 151/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 6.2902e-04 - fn: 8.5570 - fp: 3.8987 - tn: 81735.7722 - tp: 139.4430 - precision: 0.9770 - recall: 0.9470 - val_loss: 0.0065 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 152/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 4.9096e-04 - fn: 8.9873 - fp: 4.4051 - tn: 81727.1139 - tp: 147.1646 - precision: 0.9547 - recall: 0.9475 - val_loss: 0.0067 - val_fn: 19.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 50.0000 - val_precision: 0.8333 - val_recall: 0.7246\n",
      "Epoch 153/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 8.6423e-04 - fn: 11.1139 - fp: 7.8228 - tn: 81733.3418 - tp: 135.3924 - precision: 0.9311 - recall: 0.9306 - val_loss: 0.0065 - val_fn: 13.0000 - val_fp: 13.0000 - val_tn: 39791.0000 - val_tp: 56.0000 - val_precision: 0.8116 - val_recall: 0.8116\n",
      "Epoch 154/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 2s 31ms/step - loss: 5.9219e-04 - fn: 8.9747 - fp: 6.3291 - tn: 81724.8101 - tp: 147.5570 - precision: 0.9582 - recall: 0.9451 - val_loss: 0.0063 - val_fn: 16.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 53.0000 - val_precision: 0.8689 - val_recall: 0.7681\n",
      "Epoch 155/300\n",
      "78/78 [==============================] - ETA: 0s - loss: 7.0445e-04 - fn: 10.3766 - fp: 3.8701 - tn: 79724.2208 - tp: 133.5325 - precision: 0.9691 - recall: 0.9304ETA: 0s - loss: 7.2040e-04 - fn: 8.0678 - fp: 3.2203 - tn: 61327.5932 - tp: 101.1186 - precision: 0.967 - 2s 31ms/step - loss: 7.0380e-04 - fn: 10.6456 - fp: 3.9747 - tn: 81736.1646 - tp: 136.8861 - precision: 0.9692 - recall: 0.9303 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 156/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.4923e-04 - fn: 8.1646 - fp: 3.9494 - tn: 81731.7595 - tp: 143.7975 - precision: 0.9755 - recall: 0.9468 - val_loss: 0.0060 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 157/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.3828e-04 - fn: 9.1392 - fp: 3.5570 - tn: 81739.4304 - tp: 135.5443 - precision: 0.9790 - recall: 0.9366 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 158/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 6.4597e-04 - fn: 9.3291 - fp: 2.8861 - tn: 81732.8354 - tp: 142.6203 - precision: 0.9791 - recall: 0.9361 - val_loss: 0.0063 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 159/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 5.8905e-04 - fn: 11.8987 - fp: 5.7215 - tn: 81743.4684 - tp: 126.5823 - precision: 0.9652 - recall: 0.9193 - val_loss: 0.0063 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 160/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.1941e-04 - fn: 8.2532 - fp: 4.1646 - tn: 81733.4430 - tp: 141.8101 - precision: 0.9762 - recall: 0.9578 - val_loss: 0.0067 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 161/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.7367e-04 - fn: 8.5316 - fp: 7.1519 - tn: 81729.1139 - tp: 142.8734 - precision: 0.9535 - recall: 0.9557 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 162/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 8.0118e-04 - fn: 11.1646 - fp: 4.3544 - tn: 81730.3924 - tp: 141.7595 - precision: 0.9625 - recall: 0.9238 - val_loss: 0.0067 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 163/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 6.5490e-04 - fn: 11.1013 - fp: 6.7595 - tn: 81738.2911 - tp: 131.5190 - precision: 0.9486 - recall: 0.9257 - val_loss: 0.0063 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 164/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 6.5544e-04 - fn: 11.1646 - fp: 4.0633 - tn: 81737.8987 - tp: 134.5443 - precision: 0.9775 - recall: 0.9291 - val_loss: 0.0065 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 165/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.9523e-04 - fn: 8.2152 - fp: 3.9494 - tn: 81740.8354 - tp: 134.6709 - precision: 0.9740 - recall: 0.9496 - val_loss: 0.0064 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 166/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.7299e-04 - fn: 9.5443 - fp: 4.5443 - tn: 81735.3418 - tp: 138.2405 - precision: 0.9710 - recall: 0.9494 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 167/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.7111e-04 - fn: 9.5443 - fp: 4.3418 - tn: 81738.1392 - tp: 135.6456 - precision: 0.9783 - recall: 0.9472 - val_loss: 0.0065 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 168/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 7.6724e-04 - fn: 12.7215 - fp: 5.8101 - tn: 81742.8354 - tp: 126.3038 - precision: 0.9603 - recall: 0.8973 - val_loss: 0.0065 - val_fn: 16.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 53.0000 - val_precision: 0.8548 - val_recall: 0.7681\n",
      "Epoch 169/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 5.5986e-04 - fn: 10.6076 - fp: 3.3924 - tn: 81748.6835 - tp: 124.9873 - precision: 0.9796 - recall: 0.9160 - val_loss: 0.0064 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 170/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 7.1553e-04 - fn: 9.9873 - fp: 4.2658 - tn: 81749.8734 - tp: 123.5443 - precision: 0.9558 - recall: 0.9171 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 171/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 6.2941e-04 - fn: 10.2278 - fp: 6.6076 - tn: 81736.8861 - tp: 133.9494 - precision: 0.9494 - recall: 0.9393 - val_loss: 0.0068 - val_fn: 15.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 54.0000 - val_precision: 0.8182 - val_recall: 0.7826\n",
      "Epoch 172/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 7.4990e-04 - fn: 13.5316 - fp: 7.2405 - tn: 81724.9494 - tp: 141.9494 - precision: 0.9476 - recall: 0.9246 - val_loss: 0.0068 - val_fn: 16.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 53.0000 - val_precision: 0.8548 - val_recall: 0.7681\n",
      "Epoch 173/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.3244e-04 - fn: 8.8861 - fp: 4.8987 - tn: 81734.8481 - tp: 139.0380 - precision: 0.9602 - recall: 0.9488 - val_loss: 0.0065 - val_fn: 15.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 54.0000 - val_precision: 0.8438 - val_recall: 0.7826\n",
      "Epoch 174/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 4.3064e-04 - fn: 7.3165 - fp: 5.0380 - tn: 81739.3038 - tp: 136.0127 - precision: 0.9670 - recall: 0.9446 - val_loss: 0.0067 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 175/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.9040e-04 - fn: 10.5570 - fp: 6.9620 - tn: 81741.9620 - tp: 128.1899 - precision: 0.9408 - recall: 0.9173 - val_loss: 0.0067 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 176/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 6.5910e-04 - fn: 11.2278 - fp: 4.8481 - tn: 81730.6203 - tp: 140.9747 - precision: 0.9523 - recall: 0.9104 - val_loss: 0.0064 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 177/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 3.3000e-04 - fn: 7.2911 - fp: 3.6709 - tn: 81737.0886 - tp: 139.6203 - precision: 0.9720 - recall: 0.9514 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 178/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 5.2079e-04 - fn: 5.7468 - fp: 5.1772 - tn: 81736.1646 - tp: 140.5823 - precision: 0.9652 - recall: 0.9695 - val_loss: 0.0067 - val_fn: 16.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 53.0000 - val_precision: 0.8983 - val_recall: 0.7681\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 179/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 5.1085e-04 - fn: 6.4684 - fp: 4.0380 - tn: 81734.3671 - tp: 142.7975 - precision: 0.9758 - recall: 0.9634 - val_loss: 0.0065 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 180/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.4318e-04 - fn: 7.1772 - fp: 3.0127 - tn: 81734.2785 - tp: 143.2025 - precision: 0.9836 - recall: 0.9611 - val_loss: 0.0067 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 181/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 7.4366e-04 - fn: 10.6203 - fp: 7.2152 - tn: 81735.5316 - tp: 134.3038 - precision: 0.9520 - recall: 0.9145 - val_loss: 0.0070 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 182/300\n",
      "78/78 [==============================] - ETA: 0s - loss: 6.0345e-04 - fn: 8.0909 - fp: 5.3896 - tn: 79726.4156 - tp: 132.1039 - precision: 0.9525 - recall: 0.94 - 2s 30ms/step - loss: 6.0177e-04 - fn: 8.2911 - fp: 5.5316 - tn: 81738.2278 - tp: 135.6203 - precision: 0.9527 - recall: 0.9469 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 183/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.9502e-04 - fn: 6.5570 - fp: 5.1392 - tn: 81738.7215 - tp: 137.2532 - precision: 0.9596 - recall: 0.9594 - val_loss: 0.0065 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 184/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.2521e-04 - fn: 8.1392 - fp: 3.3291 - tn: 81729.4051 - tp: 146.7975 - precision: 0.9816 - recall: 0.9478 - val_loss: 0.0065 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 185/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.5986e-04 - fn: 6.6835 - fp: 6.1772 - tn: 81735.7468 - tp: 139.0633 - precision: 0.9522 - recall: 0.9602 - val_loss: 0.0064 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 186/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.0017e-04 - fn: 10.9367 - fp: 3.4937 - tn: 81734.6329 - tp: 138.6076 - precision: 0.9756 - recall: 0.9242 - val_loss: 0.0064 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 187/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.9670e-04 - fn: 6.8354 - fp: 5.8608 - tn: 81739.3165 - tp: 135.6582 - precision: 0.9597 - recall: 0.9629 - val_loss: 0.0065 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 188/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.1198e-04 - fn: 8.4684 - fp: 3.5190 - tn: 81734.7342 - tp: 140.9494 - precision: 0.9798 - recall: 0.9478 - val_loss: 0.0063 - val_fn: 15.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 54.0000 - val_precision: 0.8852 - val_recall: 0.7826\n",
      "Epoch 189/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.3850e-04 - fn: 6.7848 - fp: 3.4051 - tn: 81742.4304 - tp: 135.0506 - precision: 0.9805 - recall: 0.9609 - val_loss: 0.0064 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 190/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.9350e-04 - fn: 5.8861 - fp: 3.2152 - tn: 81741.1013 - tp: 137.4684 - precision: 0.9815 - recall: 0.9650 - val_loss: 0.0066 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 191/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 5.3424e-04 - fn: 9.2911 - fp: 6.5570 - tn: 81725.6835 - tp: 146.1392 - precision: 0.9613 - recall: 0.9430 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 192/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 8.2295e-04 - fn: 10.7089 - fp: 6.5063 - tn: 81738.6962 - tp: 131.7595 - precision: 0.9536 - recall: 0.9123 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 193/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.1019e-04 - fn: 9.2405 - fp: 5.7342 - tn: 81733.8987 - tp: 138.7975 - precision: 0.9583 - recall: 0.9381 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 194/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.0119e-04 - fn: 5.7975 - fp: 4.1646 - tn: 81732.3797 - tp: 145.3291 - precision: 0.9738 - recall: 0.9622 - val_loss: 0.0067 - val_fn: 12.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 57.0000 - val_precision: 0.8507 - val_recall: 0.8261\n",
      "Epoch 195/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 6.4560e-04 - fn: 8.8354 - fp: 5.7089 - tn: 81735.6203 - tp: 137.5063 - precision: 0.9500 - recall: 0.9446 - val_loss: 0.0064 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 196/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 4.2021e-04 - fn: 6.9747 - fp: 4.3544 - tn: 81735.4937 - tp: 140.8481 - precision: 0.9730 - recall: 0.9644 - val_loss: 0.0069 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 197/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 5.6130e-04 - fn: 9.5949 - fp: 5.8481 - tn: 81722.9620 - tp: 149.2658 - precision: 0.9528 - recall: 0.9427 - val_loss: 0.0067 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 198/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 3.9760e-04 - fn: 7.8228 - fp: 3.8101 - tn: 81735.8481 - tp: 140.1899 - precision: 0.9679 - recall: 0.9411 - val_loss: 0.0067 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 199/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 4.6766e-04 - fn: 8.8608 - fp: 4.4051 - tn: 81737.9620 - tp: 136.4430 - precision: 0.9724 - recall: 0.9500 - val_loss: 0.0068 - val_fn: 12.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 57.0000 - val_precision: 0.8382 - val_recall: 0.8261\n",
      "Epoch 200/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 4.3998e-04 - fn: 9.5696 - fp: 3.9620 - tn: 81733.8734 - tp: 140.2658 - precision: 0.9737 - recall: 0.9416 - val_loss: 0.0070 - val_fn: 15.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 54.0000 - val_precision: 0.8308 - val_recall: 0.7826\n",
      "Epoch 201/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 3.5616e-04 - fn: 6.1013 - fp: 4.6835 - tn: 81736.2278 - tp: 140.6582 - precision: 0.9344 - recall: 0.9472 - val_loss: 0.0069 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 202/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 3.7951e-04 - fn: 5.9114 - fp: 4.2152 - tn: 81737.5063 - tp: 140.0380 - precision: 0.9677 - recall: 0.9678 - val_loss: 0.0069 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 203/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 4.3438e-04 - fn: 4.8354 - fp: 6.0000 - tn: 81735.8228 - tp: 141.0127 - precision: 0.9590 - recall: 0.9708 - val_loss: 0.0066 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 204/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 2s 32ms/step - loss: 5.5578e-04 - fn: 4.5949 - fp: 6.9494 - tn: 81737.0127 - tp: 139.1139 - precision: 0.9387 - recall: 0.9775 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 205/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 7.2443e-04 - fn: 8.7089 - fp: 5.4684 - tn: 81732.4430 - tp: 141.0506 - precision: 0.9665 - recall: 0.9412 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 206/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 4.8265e-04 - fn: 7.7215 - fp: 4.6329 - tn: 81739.3671 - tp: 135.9494 - precision: 0.9710 - recall: 0.9556 - val_loss: 0.0072 - val_fn: 12.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 57.0000 - val_precision: 0.8507 - val_recall: 0.8261\n",
      "Epoch 207/300\n",
      "78/78 [==============================] - 3s 37ms/step - loss: 4.3593e-04 - fn: 8.9367 - fp: 3.6456 - tn: 81738.7089 - tp: 136.3797 - precision: 0.9767 - recall: 0.9190 - val_loss: 0.0070 - val_fn: 15.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 54.0000 - val_precision: 0.8571 - val_recall: 0.7826\n",
      "Epoch 208/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 6.4969e-04 - fn: 8.1013 - fp: 7.0000 - tn: 81742.5823 - tp: 129.9873 - precision: 0.9328 - recall: 0.9221 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 209/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 5.4979e-04 - fn: 8.3165 - fp: 4.6582 - tn: 81726.6962 - tp: 148.0000 - precision: 0.9701 - recall: 0.9465 - val_loss: 0.0067 - val_fn: 18.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 51.0000 - val_precision: 0.9273 - val_recall: 0.7391\n",
      "Epoch 210/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 5.3826e-04 - fn: 8.4177 - fp: 4.5443 - tn: 81737.1646 - tp: 137.5443 - precision: 0.9685 - recall: 0.9317 - val_loss: 0.0067 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 211/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.9246e-04 - fn: 8.3038 - fp: 4.5949 - tn: 81740.0633 - tp: 134.7089 - precision: 0.9562 - recall: 0.9317 - val_loss: 0.0067 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 212/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.3969e-04 - fn: 7.3038 - fp: 4.9114 - tn: 81726.4937 - tp: 148.9620 - precision: 0.9691 - recall: 0.9544 - val_loss: 0.0068 - val_fn: 16.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 53.0000 - val_precision: 0.8833 - val_recall: 0.7681\n",
      "Epoch 213/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 5.8887e-04 - fn: 12.7975 - fp: 4.2911 - tn: 81747.8861 - tp: 122.6962 - precision: 0.9749 - recall: 0.8657 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 214/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.8929e-04 - fn: 4.5443 - fp: 2.8354 - tn: 81737.1519 - tp: 143.1392 - precision: 0.9784 - recall: 0.9765 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 215/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.1437e-04 - fn: 5.1392 - fp: 4.8228 - tn: 81731.0633 - tp: 146.6456 - precision: 0.9546 - recall: 0.9541 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 216/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.9278e-04 - fn: 5.5316 - fp: 3.4177 - tn: 81740.5570 - tp: 138.1646 - precision: 0.9711 - recall: 0.9621 - val_loss: 0.0069 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 217/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 5.8916e-04 - fn: 7.5949 - fp: 4.3924 - tn: 81735.8228 - tp: 139.8608 - precision: 0.9701 - recall: 0.9509 - val_loss: 0.0069 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 218/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.8555e-04 - fn: 6.3038 - fp: 4.5190 - tn: 81731.1519 - tp: 145.6962 - precision: 0.9679 - recall: 0.9635 - val_loss: 0.0069 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 219/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.1535e-04 - fn: 8.5696 - fp: 1.7342 - tn: 81743.4937 - tp: 133.8734 - precision: 0.9908 - recall: 0.9413 - val_loss: 0.0067 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 220/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.5543e-04 - fn: 5.6203 - fp: 3.2911 - tn: 81733.6076 - tp: 145.1519 - precision: 0.9727 - recall: 0.9689 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 221/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 3.8577e-04 - fn: 5.6456 - fp: 5.9114 - tn: 81731.8734 - tp: 144.2405 - precision: 0.9582 - recall: 0.9727 - val_loss: 0.0067 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 222/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 3.5258e-04 - fn: 6.8228 - fp: 2.0759 - tn: 81737.5696 - tp: 141.2025 - precision: 0.9889 - recall: 0.9626 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 223/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 3.2294e-04 - fn: 5.6456 - fp: 4.3671 - tn: 81739.3291 - tp: 138.3291 - precision: 0.9672 - recall: 0.9643 - val_loss: 0.0072 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 224/300\n",
      "78/78 [==============================] - 3s 38ms/step - loss: 2.8078e-04 - fn: 4.4684 - fp: 2.9873 - tn: 81736.6709 - tp: 143.5443 - precision: 0.9826 - recall: 0.9746 - val_loss: 0.0071 - val_fn: 15.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 54.0000 - val_precision: 0.8571 - val_recall: 0.7826\n",
      "Epoch 225/300\n",
      "78/78 [==============================] - 3s 36ms/step - loss: 5.1354e-04 - fn: 5.0886 - fp: 4.9494 - tn: 81732.6962 - tp: 144.9367 - precision: 0.9480 - recall: 0.9606 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 226/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 3.5309e-04 - fn: 5.7342 - fp: 4.0253 - tn: 81733.2532 - tp: 144.6582 - precision: 0.9753 - recall: 0.9709 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 227/300\n",
      "78/78 [==============================] - 3s 35ms/step - loss: 3.8225e-04 - fn: 7.0633 - fp: 2.8228 - tn: 81731.6582 - tp: 146.1266 - precision: 0.9806 - recall: 0.9552 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 228/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 2.7685e-04 - fn: 3.5823 - fp: 2.4684 - tn: 81738.5949 - tp: 143.0253 - precision: 0.9741 - recall: 0.9759 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 229/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 2.5477e-04 - fn: 5.3291 - fp: 4.0886 - tn: 81741.1519 - tp: 137.1013 - precision: 0.9605 - recall: 0.9700 - val_loss: 0.0076 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 230/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 3.1579e-04 - fn: 3.5570 - fp: 2.6076 - tn: 81742.0000 - tp: 139.5063 - precision: 0.9696 - recall: 0.9746 - val_loss: 0.0074 - val_fn: 16.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 53.0000 - val_precision: 0.8413 - val_recall: 0.7681\n",
      "Epoch 231/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 5.8496e-04 - fn: 6.3671 - fp: 4.0380 - tn: 81735.6962 - tp: 141.5696 - precision: 0.9702 - recall: 0.9527 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 232/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 6.2598e-04 - fn: 8.4557 - fp: 6.1013 - tn: 81738.5570 - tp: 134.5570 - precision: 0.9529 - recall: 0.9421 - val_loss: 0.0075 - val_fn: 12.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 57.0000 - val_precision: 0.8507 - val_recall: 0.8261\n",
      "Epoch 233/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.9956e-04 - fn: 6.4684 - fp: 2.7215 - tn: 81743.2532 - tp: 135.2278 - precision: 0.9743 - recall: 0.9563 - val_loss: 0.0070 - val_fn: 13.0000 - val_fp: 12.0000 - val_tn: 39792.0000 - val_tp: 56.0000 - val_precision: 0.8235 - val_recall: 0.8116\n",
      "Epoch 234/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.6312e-04 - fn: 6.6076 - fp: 6.2405 - tn: 81745.1519 - tp: 129.6709 - precision: 0.9444 - recall: 0.9442 - val_loss: 0.0067 - val_fn: 13.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 56.0000 - val_precision: 0.9180 - val_recall: 0.8116\n",
      "Epoch 235/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 3.2399e-04 - fn: 5.3544 - fp: 3.4810 - tn: 81733.1772 - tp: 145.6582 - precision: 0.9692 - recall: 0.9582 - val_loss: 0.0066 - val_fn: 15.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 54.0000 - val_precision: 0.9000 - val_recall: 0.7826\n",
      "Epoch 236/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.7341e-04 - fn: 5.1139 - fp: 2.3418 - tn: 81736.0380 - tp: 144.1772 - precision: 0.9872 - recall: 0.9647 - val_loss: 0.0069 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 237/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 3.1788e-04 - fn: 6.6456 - fp: 2.3165 - tn: 81752.0000 - tp: 126.7089 - precision: 0.9829 - recall: 0.9505 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 238/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.3512e-04 - fn: 3.5570 - fp: 3.4177 - tn: 81739.2278 - tp: 141.4684 - precision: 0.9796 - recall: 0.9818 - val_loss: 0.0069 - val_fn: 16.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 53.0000 - val_precision: 0.8689 - val_recall: 0.7681\n",
      "Epoch 239/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 2.9454e-04 - fn: 4.5696 - fp: 2.4051 - tn: 81740.0506 - tp: 140.6456 - precision: 0.9828 - recall: 0.9679 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 240/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.4331e-04 - fn: 5.2405 - fp: 2.8228 - tn: 81736.9367 - tp: 142.6709 - precision: 0.9817 - recall: 0.9652 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 241/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.5439e-04 - fn: 5.4430 - fp: 2.5696 - tn: 81739.4430 - tp: 140.2152 - precision: 0.9793 - recall: 0.9629 - val_loss: 0.0066 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 242/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 4.1923e-04 - fn: 6.1772 - fp: 4.5570 - tn: 81726.5316 - tp: 150.4051 - precision: 0.9745 - recall: 0.9625 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 55.0000 - val_precision: 0.8871 - val_recall: 0.7971\n",
      "Epoch 243/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.8875e-04 - fn: 7.6835 - fp: 4.1013 - tn: 81737.6582 - tp: 138.2278 - precision: 0.9755 - recall: 0.9477 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 244/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 3.0578e-04 - fn: 7.3797 - fp: 2.4684 - tn: 81728.8608 - tp: 148.9620 - precision: 0.9889 - recall: 0.9540 - val_loss: 0.0070 - val_fn: 16.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 53.0000 - val_precision: 0.8689 - val_recall: 0.7681\n",
      "Epoch 245/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 2.6101e-04 - fn: 6.5696 - fp: 1.2405 - tn: 81736.5823 - tp: 143.2785 - precision: 0.9943 - recall: 0.9599 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 246/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.2338e-04 - fn: 6.4810 - fp: 4.2405 - tn: 81724.8608 - tp: 152.0886 - precision: 0.9711 - recall: 0.9613 - val_loss: 0.0069 - val_fn: 12.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 57.0000 - val_precision: 0.8382 - val_recall: 0.8261\n",
      "Epoch 247/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.9194e-04 - fn: 6.5443 - fp: 3.8734 - tn: 81735.9873 - tp: 141.2658 - precision: 0.9673 - recall: 0.9597 - val_loss: 0.0068 - val_fn: 14.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 55.0000 - val_precision: 0.8594 - val_recall: 0.7971\n",
      "Epoch 248/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.1878e-04 - fn: 8.4304 - fp: 4.7975 - tn: 81740.1772 - tp: 134.2658 - precision: 0.9698 - recall: 0.9371 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 249/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 2.3572e-04 - fn: 4.6329 - fp: 3.0759 - tn: 81736.3671 - tp: 143.5949 - precision: 0.9848 - recall: 0.9721 - val_loss: 0.0068 - val_fn: 13.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 56.0000 - val_precision: 0.9180 - val_recall: 0.8116\n",
      "Epoch 250/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 5.0223e-04 - fn: 6.9494 - fp: 4.8481 - tn: 81728.1139 - tp: 147.7595 - precision: 0.9667 - recall: 0.9559 - val_loss: 0.0068 - val_fn: 15.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 54.0000 - val_precision: 0.8571 - val_recall: 0.7826\n",
      "Epoch 251/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.6396e-04 - fn: 6.0127 - fp: 4.4304 - tn: 81734.8734 - tp: 142.3544 - precision: 0.9707 - recall: 0.9643 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 252/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.7575e-04 - fn: 5.6835 - fp: 1.3165 - tn: 81740.2405 - tp: 140.4304 - precision: 0.9933 - recall: 0.9564 - val_loss: 0.0070 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 253/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 5.1841e-04 - fn: 6.4557 - fp: 5.4557 - tn: 81737.9367 - tp: 137.8228 - precision: 0.9477 - recall: 0.9491 - val_loss: 0.0070 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 254/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.8743e-04 - fn: 4.6329 - fp: 4.1772 - tn: 81736.2152 - tp: 142.6456 - precision: 0.9665 - recall: 0.9681 - val_loss: 0.0073 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 255/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 4.1077e-04 - fn: 6.2785 - fp: 4.2405 - tn: 81746.1139 - tp: 131.0380 - precision: 0.9723 - recall: 0.9581 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 256/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 4.5606e-04 - fn: 6.6076 - fp: 3.9367 - tn: 81735.5949 - tp: 141.5316 - precision: 0.9652 - recall: 0.9485 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 257/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.0807e-04 - fn: 6.7468 - fp: 2.4557 - tn: 81737.6203 - tp: 140.8481 - precision: 0.9873 - recall: 0.9554 - val_loss: 0.0073 - val_fn: 16.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 53.0000 - val_precision: 0.9138 - val_recall: 0.7681\n",
      "Epoch 258/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 6.2407e-04 - fn: 8.1139 - fp: 4.0253 - tn: 81737.0380 - tp: 138.4937 - precision: 0.9775 - recall: 0.9358 - val_loss: 0.0071 - val_fn: 17.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 52.0000 - val_precision: 0.9286 - val_recall: 0.7536\n",
      "Epoch 259/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.9438e-04 - fn: 7.9241 - fp: 3.5570 - tn: 81737.0759 - tp: 139.1139 - precision: 0.9761 - recall: 0.9345 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 260/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 2.4675e-04 - fn: 4.3291 - fp: 2.4937 - tn: 81736.7975 - tp: 144.0506 - precision: 0.9862 - recall: 0.9756 - val_loss: 0.0075 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 261/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.3623e-04 - fn: 3.2785 - fp: 2.8608 - tn: 81736.4937 - tp: 145.0380 - precision: 0.9799 - recall: 0.9851 - val_loss: 0.0071 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261: 1.7541 - fp: 2.0820 - tn: 63371.2295 - tp: 112.9344 - precision: 0.980\n",
      "Epoch 262/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.1117e-04 - fn: 7.2025 - fp: 4.0000 - tn: 81731.6582 - tp: 144.8101 - precision: 0.9642 - recall: 0.9549 - val_loss: 0.0074 - val_fn: 14.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 55.0000 - val_precision: 0.8333 - val_recall: 0.7971\n",
      "Epoch 263/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 4.2458e-04 - fn: 6.4557 - fp: 4.0000 - tn: 81729.1139 - tp: 148.1013 - precision: 0.9803 - recall: 0.9643 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 264/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 4.8771e-04 - fn: 9.8228 - fp: 3.8608 - tn: 81733.3544 - tp: 140.6329 - precision: 0.9752 - recall: 0.9167 - val_loss: 0.0070 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 265/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 3.8131e-04 - fn: 5.2278 - fp: 3.7215 - tn: 81735.9367 - tp: 142.7848 - precision: 0.9821 - recall: 0.9546 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 266/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.9702e-04 - fn: 8.8101 - fp: 1.7975 - tn: 81739.5316 - tp: 137.5316 - precision: 0.9904 - recall: 0.9345 - val_loss: 0.0073 - val_fn: 15.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 54.0000 - val_precision: 0.8710 - val_recall: 0.7826\n",
      "Epoch 267/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.9196e-04 - fn: 4.4177 - fp: 2.0127 - tn: 81731.9114 - tp: 149.3291 - precision: 0.9877 - recall: 0.9743 - val_loss: 0.0073 - val_fn: 17.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 52.0000 - val_precision: 0.9286 - val_recall: 0.7536\n",
      "Epoch 268/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.1667e-04 - fn: 7.4051 - fp: 2.8608 - tn: 81734.1139 - tp: 143.2911 - precision: 0.9830 - recall: 0.9330 - val_loss: 0.0069 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 269/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 4.3977e-04 - fn: 7.6835 - fp: 4.2532 - tn: 81734.9494 - tp: 140.7848 - precision: 0.9737 - recall: 0.9545 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 270/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.9815e-04 - fn: 6.2658 - fp: 3.1013 - tn: 81742.7595 - tp: 135.5443 - precision: 0.9828 - recall: 0.9626 - val_loss: 0.0073 - val_fn: 15.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 54.0000 - val_precision: 0.8571 - val_recall: 0.7826\n",
      "Epoch 271/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.5122e-04 - fn: 4.5063 - fp: 2.4684 - tn: 81737.3038 - tp: 143.3924 - precision: 0.9827 - recall: 0.9714 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 272/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.6082e-04 - fn: 4.8608 - fp: 2.2152 - tn: 81734.6582 - tp: 145.9367 - precision: 0.9804 - recall: 0.9647 - val_loss: 0.0071 - val_fn: 14.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 55.0000 - val_precision: 0.8462 - val_recall: 0.7971\n",
      "Epoch 273/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.0934e-04 - fn: 3.5190 - fp: 2.0506 - tn: 81745.0633 - tp: 137.0380 - precision: 0.9847 - recall: 0.9804 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 274/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.9725e-04 - fn: 6.3797 - fp: 3.2911 - tn: 81740.7215 - tp: 137.2785 - precision: 0.9816 - recall: 0.9665 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 55.0000 - val_precision: 0.8730 - val_recall: 0.7971\n",
      "Epoch 275/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 1.8079e-04 - fn: 3.7215 - fp: 2.8354 - tn: 81743.6456 - tp: 137.4684 - precision: 0.9835 - recall: 0.9798 - val_loss: 0.0070 - val_fn: 14.0000 - val_fp: 6.0000 - val_tn: 39798.0000 - val_tp: 55.0000 - val_precision: 0.9016 - val_recall: 0.7971\n",
      "Epoch 276/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 2.8917e-04 - fn: 5.7215 - fp: 2.5823 - tn: 81736.2025 - tp: 143.1646 - precision: 0.9833 - recall: 0.9625 - val_loss: 0.0069 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 277/300\n",
      "78/78 [==============================] - 3s 34ms/step - loss: 2.1972e-04 - fn: 4.9494 - fp: 1.7722 - tn: 81746.0886 - tp: 134.8608 - precision: 0.9898 - recall: 0.9707 - val_loss: 0.0076 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 278/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.5845e-04 - fn: 4.9114 - fp: 3.3418 - tn: 81737.4684 - tp: 141.9494 - precision: 0.9725 - recall: 0.9728 - val_loss: 0.0075 - val_fn: 14.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 55.0000 - val_precision: 0.8333 - val_recall: 0.7971\n",
      "Epoch 279/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.5865e-04 - fn: 5.7468 - fp: 2.7215 - tn: 81740.9367 - tp: 138.2658 - precision: 0.9822 - recall: 0.9647 - val_loss: 0.0076 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 280/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.9477e-04 - fn: 4.1266 - fp: 4.1519 - tn: 81736.4684 - tp: 142.9241 - precision: 0.9644 - recall: 0.9788 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 281/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78/78 [==============================] - 2s 30ms/step - loss: 2.9937e-04 - fn: 4.7595 - fp: 4.1266 - tn: 81746.0000 - tp: 132.7848 - precision: 0.9668 - recall: 0.9707 - val_loss: 0.0070 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 282/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 4.3164e-04 - fn: 5.0633 - fp: 2.9747 - tn: 81730.5316 - tp: 149.1013 - precision: 0.9835 - recall: 0.9676 - val_loss: 0.0070 - val_fn: 12.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 57.0000 - val_precision: 0.8382 - val_recall: 0.8261\n",
      "Epoch 283/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 2.8438e-04 - fn: 4.3165 - fp: 4.9241 - tn: 81735.3418 - tp: 143.0886 - precision: 0.9633 - recall: 0.9722 - val_loss: 0.0069 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 284/300\n",
      "78/78 [==============================] - 3s 33ms/step - loss: 4.6187e-04 - fn: 6.9367 - fp: 3.6709 - tn: 81730.2785 - tp: 146.7848 - precision: 0.9789 - recall: 0.9512 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 56.0000 - val_precision: 0.8615 - val_recall: 0.8116\n",
      "Epoch 285/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 3.4625e-04 - fn: 5.1646 - fp: 1.9241 - tn: 81732.1266 - tp: 148.4557 - precision: 0.9903 - recall: 0.9585 - val_loss: 0.0067 - val_fn: 16.0000 - val_fp: 5.0000 - val_tn: 39799.0000 - val_tp: 53.0000 - val_precision: 0.9138 - val_recall: 0.7681\n",
      "Epoch 286/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.8582e-04 - fn: 5.7848 - fp: 2.4684 - tn: 81734.8734 - tp: 144.5443 - precision: 0.9856 - recall: 0.9623 - val_loss: 0.0072 - val_fn: 13.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 56.0000 - val_precision: 0.8358 - val_recall: 0.8116\n",
      "Epoch 287/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.7054e-04 - fn: 3.7848 - fp: 3.7342 - tn: 81732.8987 - tp: 147.2532 - precision: 0.9763 - recall: 0.9669 - val_loss: 0.0073 - val_fn: 12.0000 - val_fp: 11.0000 - val_tn: 39793.0000 - val_tp: 57.0000 - val_precision: 0.8382 - val_recall: 0.8261\n",
      "Epoch 288/300\n",
      "78/78 [==============================] - 3s 32ms/step - loss: 4.0374e-04 - fn: 5.1139 - fp: 3.3671 - tn: 81732.4684 - tp: 146.7215 - precision: 0.9781 - recall: 0.9714 - val_loss: 0.0070 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 289/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.0259e-04 - fn: 6.2278 - fp: 1.2785 - tn: 81739.1772 - tp: 140.9873 - precision: 0.9948 - recall: 0.9571 - val_loss: 0.0069 - val_fn: 17.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 52.0000 - val_precision: 0.8525 - val_recall: 0.7536\n",
      "Epoch 290/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 2.0781e-04 - fn: 5.2911 - fp: 2.3924 - tn: 81737.5316 - tp: 142.4557 - precision: 0.9878 - recall: 0.9697 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 291/300\n",
      "78/78 [==============================] - 2s 32ms/step - loss: 2.4903e-04 - fn: 2.6962 - fp: 2.4557 - tn: 81737.5063 - tp: 145.0127 - precision: 0.9853 - recall: 0.9870 - val_loss: 0.0074 - val_fn: 12.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 57.0000 - val_precision: 0.8769 - val_recall: 0.8261\n",
      "Epoch 292/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.6096e-04 - fn: 5.5063 - fp: 2.7342 - tn: 81740.2025 - tp: 139.2278 - precision: 0.9853 - recall: 0.9648 - val_loss: 0.0076 - val_fn: 13.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 56.0000 - val_precision: 0.8485 - val_recall: 0.8116\n",
      "Epoch 293/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 3.4443e-04 - fn: 3.5190 - fp: 3.4177 - tn: 81735.4557 - tp: 145.2785 - precision: 0.9643 - recall: 0.9807 - val_loss: 0.0074 - val_fn: 12.0000 - val_fp: 9.0000 - val_tn: 39795.0000 - val_tp: 57.0000 - val_precision: 0.8636 - val_recall: 0.8261\n",
      "Epoch 294/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.4466e-04 - fn: 4.7215 - fp: 2.0886 - tn: 81738.2785 - tp: 142.5823 - precision: 0.9861 - recall: 0.9679 - val_loss: 0.0076 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 295/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 2.2003e-04 - fn: 5.4557 - fp: 2.4304 - tn: 81735.6456 - tp: 144.1392 - precision: 0.9860 - recall: 0.9634 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 296/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 1.9871e-04 - fn: 4.1772 - fp: 0.6709 - tn: 81742.7089 - tp: 140.1139 - precision: 0.9971 - recall: 0.9604 - val_loss: 0.0066 - val_fn: 13.0000 - val_fp: 4.0000 - val_tn: 39800.0000 - val_tp: 56.0000 - val_precision: 0.9333 - val_recall: 0.8116\n",
      "Epoch 297/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 2.2539e-04 - fn: 4.4430 - fp: 0.9114 - tn: 81746.7595 - tp: 135.5570 - precision: 0.9956 - recall: 0.9613 - val_loss: 0.0071 - val_fn: 13.0000 - val_fp: 7.0000 - val_tn: 39797.0000 - val_tp: 56.0000 - val_precision: 0.8889 - val_recall: 0.8116\n",
      "Epoch 298/300\n",
      "78/78 [==============================] - 2s 29ms/step - loss: 2.7033e-04 - fn: 4.0000 - fp: 3.6456 - tn: 81734.9367 - tp: 145.0886 - precision: 0.9742 - recall: 0.9776 - val_loss: 0.0071 - val_fn: 12.0000 - val_fp: 10.0000 - val_tn: 39794.0000 - val_tp: 57.0000 - val_precision: 0.8507 - val_recall: 0.8261\n",
      "Epoch 299/300\n",
      "78/78 [==============================] - 2s 30ms/step - loss: 1.7668e-04 - fn: 2.3038 - fp: 1.4430 - tn: 81743.2532 - tp: 140.6709 - precision: 0.9886 - recall: 0.9807 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n",
      "Epoch 300/300\n",
      "78/78 [==============================] - 2s 31ms/step - loss: 1.8252e-04 - fn: 3.9747 - fp: 1.8987 - tn: 81740.3671 - tp: 141.4304 - precision: 0.9879 - recall: 0.9750 - val_loss: 0.0073 - val_fn: 13.0000 - val_fp: 8.0000 - val_tn: 39796.0000 - val_tp: 56.0000 - val_precision: 0.8750 - val_recall: 0.8116\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "#     keras.metrics.Accuracy(name='accuracy'),\n",
    "    keras.metrics.FalseNegatives(name='fn'),\n",
    "    keras.metrics.FalsePositives(name='fp'),\n",
    "    keras.metrics.TrueNegatives(name='tn'),\n",
    "    keras.metrics.TruePositives(name='tp'),\n",
    "    keras.metrics.Precision(name='precision'),\n",
    "    keras.metrics.Recall(name='recall')\n",
    "]\n",
    "\n",
    "model.compile(optimizer=keras.optimizers.Adam(1e-3), loss='binary_crossentropy', metrics=METRICS)\n",
    "\n",
    "callbacks = [keras.callbacks.ModelCheckpoint('fraud_model_at_epoch_{epoch}.h5')]\n",
    "class_weight = {0:w_p, 1:w_n}\n",
    "\n",
    "r = model.fit(\n",
    "    X_train, y_train, \n",
    "    validation_data=(X_validate, y_validate),\n",
    "    batch_size=2048, \n",
    "    epochs=300, \n",
    "#     class_weight=class_weight,\n",
    "    callbacks=callbacks,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2671/2671 [==============================] - 3s 1ms/step - loss: 0.0044 - fn: 25.0000 - fp: 17.0000 - tn: 85290.0000 - tp: 111.0000 - precision: 0.8672 - recall: 0.8162\n",
      "[0.004374184180051088, 25.0, 17.0, 85290.0, 111.0, 0.8671875, 0.8161764740943909]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15229d54d08>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 16))\n",
    "\n",
    "plt.subplot(4, 2, 1)\n",
    "plt.plot(r.history['loss'], label='Loss')\n",
    "plt.plot(r.history['val_loss'], label='val_Loss')\n",
    "plt.title('Loss Function evolution during training')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(4, 2, 2)\n",
    "plt.plot(r.history['fn'], label='fn')\n",
    "plt.plot(r.history['val_fn'], label='val_fn')\n",
    "plt.title('Accuracy evolution during training')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(4, 2, 3)\n",
    "plt.plot(r.history['precision'], label='precision')\n",
    "plt.plot(r.history['val_precision'], label='val_precision')\n",
    "plt.title('Precision evolution during training')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(4, 2, 4)\n",
    "plt.plot(r.history['recall'], label='recall')\n",
    "plt.plot(r.history['val_recall'], label='val_recall')\n",
    "plt.title('Recall evolution during training')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 100.00%\n",
      "_______________________________________________\n",
      "Classification Report:\n",
      "                  0      1  accuracy  macro avg  weighted avg\n",
      "precision      1.00   0.99      1.00       1.00          1.00\n",
      "recall         1.00   0.99      1.00       1.00          1.00\n",
      "f1-score       1.00   0.99      1.00       1.00          1.00\n",
      "support   159204.00 287.00      1.00  159491.00     159491.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[159202      2]\n",
      " [     2    285]]\n",
      "\n",
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 99.95%\n",
      "_______________________________________________\n",
      "Classification Report:\n",
      "                 0      1  accuracy  macro avg  weighted avg\n",
      "precision     1.00   0.87      1.00       0.93          1.00\n",
      "recall        1.00   0.82      1.00       0.91          1.00\n",
      "f1-score      1.00   0.84      1.00       0.92          1.00\n",
      "support   85307.00 136.00      1.00   85443.00      85443.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[85290    17]\n",
      " [   25   111]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "print_score(y_train, y_train_pred.round(), train=True)\n",
    "print_score(y_test, y_test_pred.round(), train=False)\n",
    "\n",
    "scores_dict = {\n",
    "    'ANNs': {\n",
    "        'Train': f1_score(y_train, y_train_pred.round()),\n",
    "        'Test': f1_score(y_test, y_test_pred.round()),\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n",
      "C:\\Users\\cigma\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\xgboost\\data.py:114: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
      "  \"because it will generate extra copies and increase \" +\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Result:\n",
      "================================================\n",
      "Accuracy Score: 100.00%\n",
      "_______________________________________________\n",
      "Classification Report:\n",
      "                  0      1  accuracy  macro avg  weighted avg\n",
      "precision      1.00   1.00      1.00       1.00          1.00\n",
      "recall         1.00   1.00      1.00       1.00          1.00\n",
      "f1-score       1.00   1.00      1.00       1.00          1.00\n",
      "support   159204.00 287.00      1.00  159491.00     159491.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[159204      0]\n",
      " [     0    287]]\n",
      "\n",
      "Test Result:\n",
      "================================================\n",
      "Accuracy Score: 99.96%\n",
      "_______________________________________________\n",
      "Classification Report:\n",
      "                 0      1  accuracy  macro avg  weighted avg\n",
      "precision     1.00   0.95      1.00       0.97          1.00\n",
      "recall        1.00   0.82      1.00       0.91          1.00\n",
      "f1-score      1.00   0.88      1.00       0.94          1.00\n",
      "support   85307.00 136.00      1.00   85443.00      85443.00\n",
      "_______________________________________________\n",
      "Confusion Matrix: \n",
      " [[85301     6]\n",
      " [   25   111]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgb_clf = XGBClassifier()\n",
    "xgb_clf.fit(X_train, y_train, eval_metric='aucpr')\n",
    "\n",
    "y_train_pred = xgb_clf.predict(X_train)\n",
    "y_test_pred = xgb_clf.predict(X_test)\n",
    "\n",
    "print_score(y_train, y_train_pred, train=True)\n",
    "print_score(y_test, y_test_pred, train=False)\n",
    "\n",
    "scores_dict['XGBoost'] = {\n",
    "        'Train': f1_score(y_train,y_train_pred),\n",
    "        'Test': f1_score(y_test, y_test_pred),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1522a1d8e08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_df = pd.DataFrame(scores_dict)\n",
    "\n",
    "scores_df.plot(kind='barh', figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
